Diana L. Miglioretti, PhD, Professor of Biostatistics, University of California, Davis
Screening tests are widely recommended for the early detection of disease among asymptomatic individuals. While detecting disease at an earlier stage has the potential to improve outcomes, screening also has negative consequences. including false-positive results which may lead to anxiety, unnecessary diagnostic procedures, and increased healthcare costs. Estimating the cumulative risk of false-positive results after screening rounds is important for evaluating potential screening strategies; however, it is challenging due to informative censoring due to informative censoring and competing risks. In this talk, I will present and compare several methods for estimating the cumulative risk of at least one false positive result including a semiparametric censoring bias model that allows for dependent censoring without specifying a fixed functional form for variation in risk across screening rounds. We extend these methods for estimating the risk of multiple false positive results using a non-homogeneous multi-state model to describe the screening process including competing events. We compare the performance of the methods through simulation studies, which show lower bias for the censoring bias models across most scenarios. Our methods are illustrated using data from the Breast Cancer Serveillance Consortium, which shows the cumulative risk of false positive results from repeated screening mammography is high. For example, after 10 years of annual screening, more than half of women will receive at least one false-positive recall, and more than 40% of high-risk woman will receive two or more false positive recalls.